The document outlines a project aimed at developing a human activity recognition model for road surveillance using machine learning and real-time deployment strategies. The proposed system employs a deep learning algorithm to classify activities and sends alerts to the police upon detecting abnormal behaviors like fights, supported by a mobile application for notifications and live streaming. The paper also compares existing systems with the proposed method, highlighting improvements in delay and data rate.

1. PUBLIC HUMAN ASSAULT PREDICTION USING
HUMAN ACTIVITY RECOGNTION
EMBEDDED SYSYTEM WITH MACHINE LEARNING
Presented by,
K HARI HARAN (312421106045)
B HARI PRASATH (312421106051)
Guided by,
Dr. S TEPHILLAH (M.E.,PhD)
ASSISSTANT PROFESSOR

2. OBJECTIVE
• To develop a highly accurate Human Activity recognition
model.
• To develop a efficient model for real time deployment.
• To make this model as surveillance solutions for road
surveillance.
• To develop a mobile application for providing alert to alert to
the Police in case the theft detection by detecting the human
activities.

3. LITERATURE SURVEY
3

4. 4

5. S.NO. TITLE OF
THE PAPER
WITH
AUTHOR
NAME
JOURNAL
NAME
YEAR
OF
PUBLIC
ATION
METHODOLOGY PROS &
CONS
5 An Integrated
Cloud-Based
Smart Home
Management
System with
Community
Hierarchy
IEEE
2016
6 Presentation
Attack Detection
for Face
Recognition using
Light Field
Camera
IEEE
2015
5

6. S.NO. TITLE OF THE
PAPER WITH
AUTHOR NAME
JOURNAL
NAME
YEAR OF
PUBLICA
TION
METHODOLOGY PROS &
CONS
7
8
6

7. EXISTING SYSTEM
• The proposed system uses Dual Stacked Autoencoders for Feature
Embedded Clustering (DSAFEC) and a BOW construction method based
on DSAFEC (B-DSAFEC) to improve human activity recognition from
videos.
• DSAFEC transforms video feature points into a learned feature space and
predicts their cluster assignment probabilities.
• B-DSAFEC uses these probabilities to build Bags of Words (BOWs).
• Soft clustering is applied by assigning each feature point to multiple
clusters based on the highest probabilities, reducing computational
complexity and eliminating selection restrictions.
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8. EXISTING SYSTEM vs PROPOSED
SYSTEM
Parameters Existing system Proposed system
1.Delay
2.Dat rate
3.Energy
100ms 50ms
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9. PROPOSED SYSTEM
• The project involves using the KTH video dataset to design a system for
human activity recognition in road surveillance.
• Preprocessing extracts specific frames, and features are obtained using
Pixel and Optical flow techniques.
• Data visualization is used to display these features.
• A Spatio-Temporal Net deep learning algorithm classifies human
activities.
• If abnormal behavior like fights is detected, an alert is sent to the police.
• A mobile app developed with React Native notifies the police and
enables live streaming.
• This system aims to prevent and provide evidence of abnormal activities
on roads in real-time.
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10. BLOCK DIAGRAM OF THE
PROPOSED SYSTEM
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11. BLOCK DIAGRAM DESCRIPTION
• 1. Video Input (KTH Dataset)
• Purpose: Source of video data for the system.
• 2. Preprocessing
• Purpose: Extracts specific frames for analysis.
• 3. Feature Extraction
• Purpose: Obtains features using Pixel and Optical Flow techniques.
• 4. Spatio-Temporal Net (Deep Learning Model)
• Purpose: Classifies human activities from extracted features.
• 5. Abnormal Activity Detection
• Purpose: Identifies abnormal behaviors like fights.
• 6. Alert System
• Purpose: Sends alerts to the police.
• 7. Mobile Application (React Native)
• Purpose: Notifies police and enables live streaming.
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12. CIRCUIT DIAGRAM
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13. ALGORITHM
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14. EXPECTED OUTPUT
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15. RESULTS
• Simulation output (or) Graphs or results so far
completed .
(depends on the project)
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16. CONCLUSION & FUTURE
ENHANCEMENT
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17. REFERENCES
[1] ature Embedded Learning for Human Activity Recognition Ting Wang, Student
Member, IEEE, Wing W. Y. Ng*, Senior Member, IEEE, Jinde Li, Qiuxia Wu, Member,
IEEE, Shuai Zhang, Member, IEEE, Chris Nugent, Senior Member, IEEE, Colin Shewell,
Member, IEEE[2021]
[2] PrivHome: Privacy-Preserving Authenticated Communication in Smart Home
Environment, Geong Sen Poh, Prosanta Gope, Member, IEEE , and Jianting Ning [2019,
VOL. 99, NO. 99]
[3] Continuous Human Motion Recognition With a Dynamic Range-Doppler Trajectory
Method Based on FMCW Radar, Chuanwei Ding , Student Member, IEEE, Hong Hong ,
Member, IEEE, Yu Zou, Student Member, IEEE, Hui Chu , Xiaohua Zhu, Member, IEEE,
Francesco Fioranelli , Member, IEEE, Julien Le Kernec , Senior Member, IEEE, and
Changzhi Li , Senior Member, IEEE [2019, VOL. 57, NO. 9, ]
17
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18. REFERENCES
[4] PRNU-Based Camera Attribution from Multiple Seam-Carved Images,
BSamet Taspinar, Manoranjan Mohanty, and Nasir Memon.
[2017, VOL. 20, NO. 5, ]
[5] An Integrated Cloud-Based Smart Home Management System with
Community Hierarchy, Ying-Tsung Lee, Wei-Hsuan Hsiao, Chin-Meng Huang
and Seng-Cho T. Chou. [2016, Vol No: 2162-237X]
[6] Presentation Attack Detection for Face Recognition using Light Field
Camera, R. Raghavendra Kiran B. Raja Christoph Busch Norwegian Biometric
Laboratory, Gjøvik University College, Norway. [TIP.2015.2395951]
18
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